code: def __init__(
      self,
      checkpoint_path=None,
      static=False,
      fps=25,
      pads=[0, 10, 0, 0],
      face_det_batch_size=16,
      wav2lip_batch_size=128,
      resize_factor=1,
      crop=[0, -1, 0, -1],
      box=[-1, -1, -1, -1],
      rotate=False,
      nosmooth=False,
      face_detector="sfd",
      face_enhancement=False,
  ):
      self.img_size = 96
      self.checkpoint_path = checkpoint_path
      self.static = static
      self.fps = fps
      self.pads = pads
      self.face_det_batch_size = face_det_batch_size
      self.wav2lip_batch_size = wav2lip_batch_size
      self.resize_factor = resize_factor
      self.crop = crop
      self.box = box
      self.rotate = rotate
      self.nosmooth = nosmooth
      self.face_detector = face_detector
      self.face_enhancement = face_enhancement
      if face_enhancement:
          from ppgan.faceutils.face_enhancement import FaceEnhancement
  
          self.faceenhancer = FaceEnhancement()
      makedirs("./temp", exist_ok=True)
  
file_path: PaddleHub/modules/image/Image_gan/gan/wav2lip/model.py
imports: from os import listdir, path, makedirs
  import platform
  import numpy
  import scipy, cv2, os, sys, argparse
  import json, subprocess, random, string
  from tqdm import tqdm
  from glob import glob
  import paddle
  from paddle.utils.download import get_weights_path_from_url
  from ppgan.faceutils import face_detection
  from ppgan.utils import audio
  from ppgan.models.generators.wav2lip import Wav2Lip
label_desc: Use image node_id 1 to create a new Wav2LipPredictor object  with example: obj = Wav2LipPredictor(checkpoint_path, static, fps, pads, face_det_batch_size, wav2lip_batch_size, resize_factor, crop, box, rotate, nosmooth, face_detector, face_enhancement)